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Whole building energy model (BEM) is a physics-based modeling method for building energy simulation. It has been widely used in the building industry for code compliance, building design optimization, retrofit analysis, and other uses. Recent research also indicates its strong potential for the control of heating, ventilation and air-conditioning (HVAC) systems. However, its high-order nature and slow computational speed limit its practical application in real-time HVAC optimal control. Therefore, this study proposes a practical control framework (named BEM-DRL) that is based on deep reinforcement learning. The framework is implemented and assessed in a novel radiant heating system in an existing office building as a case study. The complete implementation process is presented in this study, including: building energy modeling for the novel heating system, multi-objective BEM calibration using the Bayesian …